Person identification using ocular biometrics with liveness detection

ABSTRACT

A method of assessing the identity of a person by one or more of: internal non-visible anatomical structure of an eye represented by the Oculomotor Plant Characteristics (OPC), brain performance represented by the Complex Eye Movement patterns (CEM), iris patterns, and periocular information. In some embodiments, a method of making a biometric assessment includes measuring eye movement of a subject, making an assessment of whether the subject is alive based on the measured eye movement, and assessing a person&#39;s identity based at least in part on the assessment of whether the subject is alive. In some embodiments, a method of making a biometric assessment includes measuring eye movement of a subject, assessing characteristics from the measured eye movement, and assessing a state of the subject based on the assessed characteristics.

PRIORITY CLAIM

This application is a continuation-in-part of International ApplicationNo. PCT/US2012/30912 Entitled: “PERSON IDENTIFICATION USING OCULARBIOMETRICS”, filed on Mar. 28, 2012, the disclosure of which isincorporated herein by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under award no.60NANB10D213 awarded by the National Institute of Standards, theNational Science Foundation CAREER Grant #CNS-1250718, the NationalInstitute of Standards and Technology Grants #60NANB10D213 and#60NANB12D234, and the National Science Foundation GRFP Grant#DGE-1144466. The government has certain rights in the invention.

BACKGROUND

1. Field

This disclosure is generally related to person identification, and morespecifically to methods and systems for identifying persons using ocularbiometric information.

2. Description of the Related Art

Accurate, non-intrusive, and fraud-resistant identity recognition is anarea of increasing concern in today's networked world, with the need forsecurity set against the goal of easy access. Many commonly used methodsfor identity determination have known problems. For example, passwordverification has demonstrated many weaknesses in areas of accuracy (theindividual typing the password may not actually be its owner), usability(people forget passwords), and security (people write passwords down orcreate easy-to-hack passwords).

The communication between a human and a computer frequently begins withan authentication request. During this initial phase of interaction auser supplies a system with verification of his/her identity, frequentlygiven in the form of a typed password, graphically encoded securityphrase, or a biometric token such as an iris scan or fingerprint. Incases when the user is prompted to select the identification key from asequence of numerical and graphical symbols, there is a danger ofaccidental or intentional shoulder surfing performed directly or by useof a hidden camera. Moreover, such challenges may become specificallypronounced in cases of multi-user environments includingshared-workstation use and more contemporary interaction media such astabletops. Authentication methods requiring remembrance of informationsuch as symbols and photos have reduced usability, due to the fact thatlong, sophisticated passwords can be easily forgotten and shortpasswords are easy to break. Even biometric methods such as iris andfinger print-based authentication may not be completely fraud-proof,since they are based on a human's body characteristics that can bereplicated.

There are a number of methods employed today for biometric purposes.Some examples include the use of fingerprints, iris, retina scans, facerecognition, hand/finger geometry, brain waves, periocular features, earshape, gait, and voice recognition. Iris-based identification isconsidered to be one of the most accurate among existing biometricmodalities. However, commercial iris-identification systems may be easyto spoof, and they are also inconvenient and intrusive since theyusually require a user to stand very still and very close to the imagecapturing device.

The human eye includes several anatomical components that make up theoculomotor plant (OP). These components include the eye globe and itssurrounding tissues, ligaments, six extraocular muscles (EOMs) eachcontaining thin and thick filaments, tendon-like components, varioustissues and liquids.

The brain sends a neuronal control signal to three pairs of extraocularmuscles, enabling the visual system to collect information from thevisual surround. As a result of this signal, the eye rotates in itssocket, exhibiting eye movement such as the following types: fixation,saccade, smooth pursuit, optokinetic reflex, vestibulo-ocular reflex,and vergence. In a simplified scenario, when a stationary person views atwo-dimensional display (e.g., computer screen), three eye movementtypes are exhibited: fixations (maintaining the eye directed on thestationary object of interest), saccades (rapid eye rotations betweenpoints of fixation with velocities reaching 700°/s), and smooth pursuit(movements that occur when eyes are tracking a smooth moving object).

Accurate estimation of oculomotor plant characteristics is challengingdue to the secluded nature of the corresponding anatomical components,which relies on indirect estimation and includes noise and inaccuraciesassociated with the eye tracking equipment, and also relies on effectiveclassification and filtering of the eye movement signal.

In some cases, an intruder may carry out a coercion attack in which agenuine user is forced to log into a secure terminal (e.g., using aremote connection) under duress. Some approaches for preventing coerciveattacks are easily observable (for example, typed passwords or voicecommands), or intrusive (for example, skin conductance sensors).

Many biometric technologies are susceptible to attacks in which fakedhuman features (for example, fake fingerprints, facial images, or irisimages) are successfully as passed off as authentic. For example, somecommercial iris-identification systems can be spoofed by high resolutionimages printed on placards with small holes in the images to bypassliveness tests, fingerprints can be spoofed with common householdarticles such as gelatin, and face recognition systems can be spoofedwith printed face images. In certain cases, a spoofing attack involvespresenting an accurate mechanical replica of the human eye is presentedto the sensor. Such replicas may perform the eye movements similar tothat of a human.

SUMMARY

In an embodiment, a multi-modal method of assessing the identity of aperson includes measuring eye movement of the person and measuringcharacteristics of an iris or/and periocular information of a person.Based on measured eye movements, estimates may be made ofcharacteristics of an oculomotor plant of the person, complex eyemovement patterns representing brain's control strategies of visualattention, or both. Complex eye movement patterns may include, forexample, a scanpath of the person's eyes including a sequence offixations and saccades. The person's identity may be assessed based onthe estimated characteristics of the oculomotor plant, the estimatedcomplex eye movement patterns, and the characteristics of the iris ofthe person or/and periocular information. The identity assessment may beused to authenticate the person (for example, to allow the person accessto a computer system or access to a facility).

In an embodiment, a method of assessing a person's identity includesmeasuring eye movements of the person. Based on measured eye movements,estimates are made of characteristics of an oculomotor plant of theperson and complex eye movement patterns of the person's eyes. Theperson's identity may be assessed based on the estimated characteristicsof the oculomotor plant and the estimated complex eye movement patternsthat are representative of the brain's control strategies of visualattention.

In an embodiment, a method of assessing a person's identity includesmeasuring eye movements of the person while the person is looking atstimulus materials. In various embodiments, for example, the person maybe reading, looking at various pictures, or looking at a jumping dot oflight. Estimates of characteristics of an oculomotor plant are madebased on the recorded eye movements.

In an embodiment, a system for assessing the identity of a personincludes a processor, a memory coupled to the processor, and aninstrument (e.g. image sensor such as web-camera) that can measure eyemovement of a person and external ocular characteristics of the person(such as iris characteristics or periocular information). Based onmeasured eye movements, the system can estimate characteristics of anoculomotor plant of the person, strategies employed by the brain toguide visual attention represented via complex eye movement patterns, orboth. The system can assess the person's identity based on the estimatedcharacteristics of the oculomotor plant, brain strategies to guidevisual attention via complex eye movement patterns, and the externalocular characteristics of the person.

In an embodiment, a method of making a biometric assessment includesmeasuring eye movement of a subject, making an assessment of whether thesubject is alive based on the measured eye movement, and assessing aperson's identity based at least in part on the assessment of whetherthe subject is alive.

In an embodiment, a method of making a biometric assessment includesmeasuring eye movement of a subject, assessing characteristics from themeasured eye movement, and assessing a state of the subject based on theassessed characteristics.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates one embodiment of assessing a person's identity usingmultimodal ocular biometrics based on eye movement tracking andmeasurement of external characteristics.

FIG. 2 illustrates one embodiment of authentication using oculomotorplant characteristics, complex eye movement patterns, iris andperiocular information.

FIG. 3 is a block diagram illustrating architecture for biometricauthentication via oculomotor plant characteristics according to oneembodiment.

FIG. 4 illustrates raw eye movement signal with classified fixation andsaccades and an associated oculomotor plant characteristics biometrictemplate.

FIG. 5 is a graph illustrating receiver operating curves for ocularbiometric methods in one experiment.

FIG. 6 illustrates one embodiment of a system for allowing remotecomputing with ocular biometric authentication of a user.

FIG. 7 illustrates one embodiment of a system for allowing remotecomputing with ocular biometric authentication of a user wearing aneye-tracking headgear system.

FIG. 8 is a set of graphs illustrating examples of complex oculomotorbehavior.

FIG. 9 illustrates a spoof attack via pre-recorded signal from theauthentic user.

FIG. 10 illustrates eye movement for an authentic, live user.

FIG. 11 illustrates an example of the difference between “normal” and“coercion” logins.

FIG. 12 illustrates a second example of the difference between “normal”and “coercion” logins.

FIG. 13 illustrates biometric assessment with subject state detectionand assessment.

FIG. 14 illustrates a comparative distribution of fixation over multiplerecording sessions.

FIGS. 15A and 15B are graphs of a receiver operating characteristic inwhich true positive rate is plotted against false acceptance rate forseveral fusion methods.

FIGS. 16A and 16B are graphs of a cumulative match characteristic forseveral fusion methods.

While the invention is described herein by way of example for severalembodiments and illustrative drawings, those skilled in the art willrecognize that the invention is not limited to the embodiments ordrawings described. It should be understood, that the drawings anddetailed description thereto are not intended to limit the invention tothe particular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope of the present invention as defined by the appendedclaims. The headings used herein are for organizational purposes onlyand are not meant to be used to limit the scope of the description orthe claims. As used throughout this application, the word “may” is usedin a permissive sense (i.e., meaning having the potential to), ratherthan the mandatory sense (i.e., meaning must). Similarly, the words“include”, “including”, and “includes” mean including, but not limitedto.

DETAILED DESCRIPTION OF EMBODIMENTS

As used herein, “oculomotor plant” means the eye globe and itssurrounding tissues, ligaments, and extraocular muscles (EOMs), each ofwhich may contain thin and thick filaments, tendon-like components,various tissues and liquids.

As used herein, “scanpath” means a spatial path formed by a sequence offixations and saccades. Fixations occur when the eye is held in arelatively stable position, allowing heightened visual acuity on anobject of interest. Saccades may occur when the eye rotates quickly, forexample, between points of fixation, with almost no visual acuitymaintained during rotation. Velocities during saccades may reach as highas 700° per second.

As used herein, “brain control strategies” are defined as an ability ofthe brain to guide the eye to gather the information from thesurrounding world. Strategies may be based on, or include, informationon how and where the eye is guided. Brain control strategies canmanifest themselves in the spatial and temporal (e.g. location andduration) characteristics of fixation, such characteristics of saccadesas main-sequence relationship (relationship between maximum velocityexhibited during a saccade and its amplitude), amplitude durationrelationship (relationship between saccade's duration and itsamplitude), saccade's waveform (relationship between the time it takesto reach a peak velocity during a saccade to the total saccade duration)and other characteristics.

As used herein, “complex eye movement (CEM) patterns” are defined as eyemovement patterns and characteristics that allow inferring brain'sstrategies or activity to control visual attention. This informationmight be inferred from individual and aggregated characteristics of ascanpath. In addition CEM can include, for example, the informationabout saccades elicited in response to different stimuli. Examples offorms in which CEM information may be manifested include: simpleundershoot or overshoot (e.g. saccades that miss the target and nocorrection is made to put gaze location on the target), correctedundershoot/overshoot (e.g. saccades that miss the target, but the braincorrects eye position to the target's position), multi-correctedundershoot/overshoot—similar in definition to the correctedundershoot/overshoot saccade however additional series of correctivesaccades is added that brings the resulting fixation position closer tothe target; dynamic overshoot which is the oppositely directedpost-saccadic eye movement in the form of backward jerk at the offset ofa saccade; compound saccade which represented by an initial saccade thatis subsequently followed by two or more oppositely directed saccades ofsmall amplitude that move the eye-gaze back and forth from the targetposition; and express saccade which is represented by a sequence ofsaccades directed toward the target where the end of the initial saccadeis in the small spatial and temporal proximity from the sequence of newsaccades leading to the target.

As used herein, “assessing a person's identity” includes determiningthat a person being assessed or measured is a particular person orwithin a set or classification or persons. “Assessing a person'sidentity” also includes determining that a person being assessed is nota particular person or within a set or classification or persons (forexample, scanning eye movements of Person X to determine whether or notPerson X is on a list a persons authorized to access to a computersystem).

In some embodiments, a person's identity is assessed using one or morecharacteristics that exist only in a live individual. The assessment maybe used, for example, to authenticate the person for access to a systemor facility. In certain embodiments, authentication of a person does notrequire the person being authenticated to remember any information (forexample, to remember a password).

In some embodiments, a person's identity is assessed using measurementsof one or more visible characteristics of the person in combination withestimates of one or more non-visible characteristics of the person. Theassessment may be used to authenticate the person for access a computersystem, for example.

In some embodiments, a method of assessing a person's identity includesmaking estimates based on eye movements of a person and measuring irischaracteristics or periocular information of the person. Eye movementsmay be used to estimate oculomotor plant characteristics, brain controlstrategies in a form of complex eye movement patters and scanpaths, orall these characteristics. FIG. 1 illustrates one embodiment ofassessing a person's identity using multimodal ocular biometrics basedon eye movement tracking and measurement of external characteristics. At100, eye movements of a person are tracked. Eye movement data may becollected using, for example, an eye tracking instrument.

At 102, acquired eye movement data may be used to estimate oculomotorplant characteristics. Dynamic and static characteristics of theoculomotor plant that may be estimated include the eye globe's inertia,dependency of an individual muscle's force on its length and velocity ofcontraction, resistive properties of the eye globe, muscles andligaments, characteristics of the neuronal control signal sent by thebrain to the EOMs, and the speed of propagation of this signal.Individual properties of the EOMs may vary depending on their roles. Forexample, the agonist role may be associated with the contracting musclethat pulls the eye globe in the required direction, while the antagonistrole may be associated with the lengthening muscle resisting the pull.

At 104, acquired eye movement data may be used to analyze complex eyemovements. The CEM may be representative of the brain's controlstrategies of guiding visual attention. Complex eye movement patternsmay be based on, for example, on individual or aggregated scanpath data.Scanpaths may include one or more fixations and one or more saccades bya person's eye. The processed fixation and saccade groups may describethe scanpath of a recording. Individual scanpath metrics may becalculated for each recording based on the properties of its uniquescanpath. Basic eye movement metrics may include: fixation count,average fixation duration, average vectorial average vertical saccadeamplitude, average vectorial saccade velocity, average vectorial saccadepeak velocity, and the velocity waveform indicator (Q), and a variety ofsaccades such as: undershot/overshoot, corrected undershoot/overshoot,multi-corrected undershoot/overshoot, dynamic, compound, and expresssaccades. More complex metrics, resulting from the aggregated scanpathdata, may include: scanpath length, scanpath area, regions of interest,inflection count, and slope coefficients of the amplitude-duration andmain sequence relationships.

At 106, measurements may be taken of external characteristics of theperson. In one embodiment, one or more characteristics of the person'siris or/and periocular information are measured. In certain embodiments,non-ocular external characteristics, such as a facial characteristics orfingerprints, may be acquired in addition to, or instead of externalocular characteristics. At 108, the measurements acquired at 106 areused to assess external characteristics of a person.

At 110, a biometric assessment is performed based on some or all of theestimated oculomotor plant characteristics, complex eye movementpatterns, and external ocular characteristics. In some embodiments,biometric assessment is based on a combination of one or more dynamiccharacteristics is combined with one or more static traits, such as irispatterns or periocular information. Authentication of a person may becarried out based on a combination of two or more of: oculomotor plantcharacteristics, complex eye movement patterns, and external ocularcharacteristics.

In some embodiments, a single instrument is used to acquire all of theeye movement data and external characteristic data (for example, irispatterns or/and periocular information) for a person. In otherembodiments, two or more different instruments may be used to acquireeye movement data or external characteristic data for a person.

Methods and systems as described herein may be shoulder-surfingresistant. For example, data presented during authentication proceduresas described herein may not reveal any information about a user to anoutside observer. In addition, methods and systems as described hereinmay be counterfeit-resistant in that, for example, they can be based oninternal non-visible anatomical structures or complex eye movementpatters representative of the brain's strategies to guide visualattention. In some embodiments, information on OPC and CEM biometricused in combination with one another to assess identity of a person.

In some embodiments, a user is authenticated by estimating individualoculomotor plant characteristics (OPC) and complex eye movement patternsgenerated for a specific type of stimulus. The presented visualinformation may be used to evoke eye movements that facilitateextraction of the OPC and CEM. The information presented can be overseenby a shoulder-surfer with no negative consequences. As a result, theauthentication does not require any feedback from a user except lookingat a presented sequence of images or text.

FIG. 2 illustrates one embodiment of authentication using OPC, CEM,iris, and periocular information. The OPC, CEM, iris, and periocularinformation may be captured by a single camera sensor. Identityassessment 200 includes use of image sensor 201 and eye trackingsoftware 203. From image data captured with image sensor 201, eyetracking software 203 may generate raw eye positional signal data, whichmay be sent to the OPC and the CEM modules, and eye images, which may besent to iris module 205 and periocular module 207. In general, allmodules may process the input in the form of raw eye position signal oreye images, perform feature extraction, generate biometric templates,perform individual trait template matching 206, multi-trait templatematching phase 208, and decision output 210. Feature extraction 204includes OPC feature extraction 211, CEM feature extraction 213, irisfeature extraction 215, and periocular feature extraction 217.Processing of eye images includes iris module image pre-processing 231,periocular module image pre-processing 232, iris module templategeneration 233,

At 202, eye positional signal information is acquired. Raw eye movementdata produced during a recording is supplied to an eye movementclassification module at 212. In some embodiments, an eye-tracker sendsthe recorded eye gaze trace to an eye movement classification algorithmat 212 after visual information employed for the authentication ispresented to a user. An eye movement classification algorithm mayextract fixations and saccades from the signal. The extracted saccades'trajectories may be supplied to the mathematical model of the oculomotorplant 214 for the purpose of simulating the exact same trajectories. At216, an optimization algorithm modifies the values for the OPC toproduce a minimum error between the recorded and the simulated signal.The values that produce the minimum error are supplied to anauthentication algorithm at 218. The authentication algorithm may bedriven by a Hotteling's T-square test 220. Templates may be accessiblefrom template database 221. The Hotteling's T-square test (or some otherappropriate statistical test) may either accept or reject the user fromthe system. An authentication probability value (which may be derived,for example, by the Hotteling's T-square test) may be propagated todecision fusion module 222. Although in the embodiment shown in FIG. 2,a Hotteling's T-square test is employed, an authentication algorithm maybe driven by other suitable statistical tests. In one embodiment, anauthentication algorithm uses a Student's t-test is used (which may beenhanced by voting).

Fusion module 222 may accept or reject a person based on one or moresimilarity scores. In some case, fusion module 222 accept or reject aperson based on OPC similarity score 224, CEM similarity score 226, irissimilarity score 270, and periocular similarity score 280. Furtheraspects of implementing authentication based on OPC and the othermodalities are set forth below.

Eye Movement Classification:

At 212, a Velocity-Threshold (I-VT) classification algorithm (or someother eye movement classification algorithm) may be employed withthreshold selection accomplished via standardized behavior scores. Afterthe classification saccades with amplitudes smaller than 0.5°(microsaccades) may be filtered out to reduce the amount of noise in therecorded data.

Oculomotor Plant Mathematical Model:

At 214, a linear horizontal homeomorphic model of the oculomotor plantcapable of simulating the horizontal and vertical component of eyemovement during saccades may be employed. The model mathematically mayrepresent dynamic properties of the OP via a set of linear mechanicalcomponents such as springs and damping elements. The followingproperties may be considered for two extraocular muscles that aremodeled (medial and lateral recti) and the eye globe: active statetension—tension developed as a result of the innervations of an EOM by aneuronal control signal, length tension relationship—the relationshipbetween the length of an EOM and the force it is capable of exerting,force velocity relationship—the relationship between the velocity of anEOM extension/contraction and the force it is capable of exerting,passive elasticity—the resisting properties of an EOM not innervated bythe neuronal control signal, series elasticity—resistive properties ofan EOM while the EOM is innervated by the neuronal control signal,passive elastic and viscous properties of the eye globe due to thecharacteristics of the surrounding tissues. The model may take as aninput a neuronal control signal, which may be approximated by apulse-step function. The OPC described above can be separated into twogroups, each separately contributing to the horizontal and the verticalcomponents of movement.

OPC Estimation Algorithm:

At 230, a Nelder-Mead (NM) simplex algorithm (or some other minimizationalgorithm such as Trust-Region using the interior-reflective Newtonmethod) may be used in a form that allows simultaneous estimation of allOPC vector parameters at the same time. A subset of some OPC may beempirically selected. The remaining OPC may be fixed to default values.In an example a subset of selected OPC comprises of length tension—therelationship between the length of an extraocular muscle and the forceit is capable of exerting, series elasticity—resistive properties of aneye muscle while the muscle is innervated by the neuronal controlsignal, passive viscosity of the eye globe, force velocityrelationship—the relationship between the velocity of an extraocularmuscle extension/contraction and the force it is capable of exerting—inthe agonist muscle, force velocity relationship in the antagonistmuscle, agonist and antagonist muscles' tension intercept that ensuresan equilibrium state during an eye fixation at primary eye position (forexample an intercept coefficient in a linear relationship between theforce that a muscle applies to the eye and the rotational position ofthe eye during fixation), the agonist muscle's tension slope (forexample, a slope coefficient in a linear relationship between the forcethat an agonist muscle applies to the eye and the rotation position ofthe eye during fixation), the antagonist muscle's tension slope (forexample, a tension slope coefficient for the antagonist muscle), and eyeglobe's inertia. Lower and upper boundaries may be imposed to preventreduction or growth of each individual OPC value to less than 10% orlarger than 1000% of its default value. Stability degradation of thenumerical solution for differential equations describing the OPMM may beused as an additional indicator for acceptance of the suggested OPCvalues by the estimation algorithm. In some embodiments, a templateincluding some or all of the OPC described above is passed to a matchingmodule to produce a matching score between a computed template and atemplate already stored in the database.

Authentication:

As an input, the person authentication algorithm takes a vector of theOPC optimized for each qualifying saccade. In some embodiments, astatistical test is applied to assess all optimized OPC in the vector atthe same time. In the example shown in FIG. 2, a Hotelling's T-squaretest is applied. The test may assess data variability in a singleindividual as well as across multiple individuals. In one embodiment,the Hotelling's T-square test is applied to an empirically selectedsubset of five estimated parameters: series elasticity, passiveviscosity of the eye globe, eye globe's inertia, agonist muscle'stension slope, and the antagonist muscle's tension slope.

As a part of the authentication procedure, the following Null Hypothesis(H0) is formulated assuming datasets i and j may be compared: “H0:Thereis no difference between the vectors of OPC between subject i and j”.The statistical significance level (p) resulting from the Hotelling'sT-square test may be compared to a predetermined threshold (for example,0.05). In this example, if the resulting p is smaller than thethreshold, the H0 is rejected indicating that the datasets in questionbelonged to different people. Otherwise, the H0 is accepted indicatingthat the datasets belonged to the same person. Two types of errors maybe recorded as a result: (1) the rejection test of the H0 when thedatasets belonged to the same person; and (2) the acceptance test of theH0 when the datasets were from different people.

In the method described above, variability was accounted for by applyinga Hotelling's T-square test. In certain embodiments, oculomotor plantcharacteristics are numerically evaluated given a recorded eye-gazetrace.

Referring to the CEM side of FIG. 2, aspects of biometrics using CEM aredescribed. In some embodiments, some aspects of biometrics using CEM ina form of scanpaths are as described in C. Holland, and O. V.Komogortsev, Biometric Identification via Eye Movement Scanpaths inReading, In Proceedings of the IEEE International Joint Conference onBiometrics (IJCB), 2011, pp. 1-8. As noted above, raw eye movement dataproduced during a recording is supplied to an eye movementclassification module at 212. Classified fixations and saccades formingcomplex eye movement patterns may be processed by two modules:individual scanpath component module 240 and aggregated scanpath module241. Individual scanpath component module 240 may process eye movementcharacteristics belonging to individual fixations and saccades.Characteristics processed by the individual scanpath component module240 may include the following:

-   -   Fixation Count—number of detected fixations. Fixation count is        indicative of the number of objects processed by the subject,        and was measured simply as the total number of fixations        contained within the scanpath.    -   Average Fixation Duration—sum of duration of all fixations        detected divided by fixation count. Average fixation duration is        indicative of the amount of time a subject spends interpreting        an object, and was measured as the sum of fixation durations        over the fixation count.    -   Average Vectorial Saccade Amplitude—sum of vectorial saccade        amplitudes over the total number of saccades, where the        vectorial amplitude of a saccade was defined as the Euclidean        norm of the horizontal and vertical amplitudes. There is a noted        tendency for saccades to maintain similar amplitudes during        reading, average saccade amplitude was considered as a candidate        biometric feature under the assumption that differences in        amplitude may be apparent between subjects. Average vectorial        saccade amplitude was measured as the sum of vectorial saccade        amplitudes over the total number of saccades, where the        vectorial amplitude of a saccade was defined as the Euclidean        norm of the horizontal and vertical amplitudes, according to the        equation:

${{Vectorial}\mspace{14mu} {Average}} = \frac{\sum\limits_{i = 1}^{n}\sqrt{x_{i}^{2} + y_{i}^{2}}}{n}$

-   -   Average Horizontal Saccade Amplitude—average amplitude of the        horizontal component of saccadic movement. Horizontal saccade        amplitude was considered separately as these are more indicative        of between-word saccades. Average horizontal saccade amplitude        was measured as the sum of horizontal saccade amplitudes greater        than 0.5° over the total number of horizontal saccades with        amplitude greater than 0.5°.    -   Average Vertical Saccade Amplitude—average amplitude of the        vertical component of saccadic movement. Vertical saccade        amplitude was considered separately as these are more indicative        of between-line saccades. Average vertical saccade amplitude was        measured as the sum of vertical saccade amplitudes greater than        0.5° over the total number of vertical saccades with amplitude        greater than 0.5°.    -   Average Vectorial Saccade Velocity—sum of vectorial saccade        velocities over the total number of saccades, where the        vectorial velocity of a saccade was defined as the Euclidean        norm of the horizontal and vertical velocities. Average        vectorial saccade velocity as measured as the sum of vectorial        saccade velocities over the total number of saccades, where the        vectorial velocity of a saccade was defined as the Euclidean        norm of the horizontal and vertical velocities.    -   Average Vectorial Saccade Peak Velocity—sum of vectorial saccade        peak velocities over the total number of saccades. Average        vectorial saccade peak velocity was measured as the sum of        vectorial saccade peak velocities over the total number of        saccades, where the vectorial peak velocity of a saccade was        defined as the Euclidean norm of the horizontal and vertical        peak velocities.    -   Velocity Waveform Indicator (Q)—the relationship between the        time it takes to reach a peak velocity during a saccade to the        total saccade duration. We use the term velocity waveform        indicator (Q) to refer to the ratio of peak velocity to average        velocity of a given saccade. In normal human saccades this value        is roughly constant at 1.6, though it is assumed that this is        subject to some amount of variation similar to the        amplitude-duration and main sequence relationships. A rough        estimate of this value may be obtained from the ratio of the        average vectorial peak velocity over the average vectorial        velocity.    -   Amplitude-Duration Relationship—the relationship between the        amplitude of the saccade and its duration.    -   Coefficient of the Amplitude-Duration Relationship. The        amplitude-duration relationship varies from person to person,        and describes the tendency for saccade duration to increase        linearly with amplitude, according to the equation:

Duration=C×|Amplitude|+Duration_(min)

-   -   To calculate the slope coefficient of this relationship, a data        set may be constructed from the saccade groups such that        x-column data contained the larger absolute component        (horizontal or vertical) amplitude and y-column data contained        the respective saccade duration.    -   The slope coefficient of the amplitude-duration relationship may        be obtained from a linear regression of this data set.    -   Main Sequence Relationship—the relationship between the        amplitude of the saccade and its peak velocity.    -   Coefficient of the Main Sequence Relationship. The main sequence        relationship varies from person to person, and describes the        tendency for saccade peak velocity to increase exponentially        with amplitude, according to the equation:

${{Peak}\mspace{14mu} {Velocity}} = {{Velocity}_{\max}\left( {1 - ^{\frac{{Amplitude}}{c}}} \right)}$

-   -   This relationship has shown to be roughly linear for small        saccades in the range of 0-10° amplitude. As a result, a linear        approximation may be acceptable in the current context, as the        saccades produced during reading are often on the order of 0-3°        amplitude, with very few over 10° amplitude.    -   To calculate the slope coefficient of this relationship, a data        set may be constructed from the saccade groups such that        x-column data contained absolute component (horizontal or        vertical) amplitude and y-column data contained the respective        absolute component peak velocity. The slope coefficient of the        main sequence relationship may be obtained from a linear        regression of this data set.

Characteristics processed by the aggregated scanpath module 241 mayinclude the following:

-   -   Scanpath Length—summated amplitude of all detected saccades.        Scanpath length is indicative of the efficiency of visual        search, and may be considered as a candidate biometric feature        under the assumption that visual search is dependent on the        subject's familiarity with similar patterns/content. Scanpath        length may be measured as the sum of absolute distances between        the vectorial centroid of fixation points, where the vectorial        centroid was defined as the Euclidean norm of the horizontal and        vertical centroid positions, according to the equation:

Scanpath Length=Σ_(i=2) ^(n)|√{square root over (x _(i) ² +y _(i)²)}−√{square root over (x _(i-1) ² +y _(i-1) ²)}|

-   -   Scanpath Area—area that is defined by a convex hull that is        created by fixation points. Scanpath area may be measured as the        area of the convex hull formed by fixation points. Scanpath area        is similar to scanpath length in its indication of visual search        efficiency, but may be less sensitive to localized searching.        That is, a scanpath may have a large length while only covering        a small area.    -   Regions of Interest—total number of spatially unique regions        identified after applying a spatial mean shift clustering        algorithm to the sequence of fixations that define a scanpath    -   Regions of interest may be measured as the total number of        spatially unique regions identified after applying a spatial        mean shift clustering algorithm to the fixation points of the        scanpath, using a sigma value of 2° and convergence resolution        of 0.1°.    -   Inflection Count—number of eye-gaze direction shifts in a        scanpath. Inflections occur when the scanpath changes direction,        in reading there are a certain amount of “forced” inflections        that may be necessary to progress through the text, but general        differences in inflection count are indicative of attentional        shifts. Inflection count may be measured as the number of        saccades in which the horizontal and/or vertical velocity        changes signs, according to the following algorithm:

1. Inflections=0

2. i=2

3. While i<Saccade Count:

4. If sign(Velocity_(i))!=sign(Velocity_(i-1)):

5. Inflections=Inflections+1

6. End if

7. i=i+1

8. End while

-   -   Scanpath fix—aggregated representation of a scanpath that is        defined by fixation points and their coordinates.

OPC biometric template 242 and scanpath biometric template 244 may betested for match/non-match. Characteristics may be compared usingGaussian cumulative distribution function (CDF) 246. In some cases, allcharacteristics except the scanpath_fix are compared via Gaussiancumulative distribution function (CDF) 246.

To determine a relative measure of similarity between metrics, aGaussian cumulative distribution function (CDF) was applied as follows,were x and μ are the metric values being compared and a is themetric-specific standard deviation:

$p = {\frac{1}{\sigma \sqrt{2\pi}}{\int_{- \infty}^{x}{^{- \frac{t - \mu}{2\sigma^{2}}}\ {t}}}}$

A Gaussian CDF comparison produces a probability value between 0 and 1,where a value of 0.5 indicates an exact match and a value of 0 or 1indicates no match. This probability may be converted into a moreintuitive similarity score, where a value of 0 indicates no match andvalues of 1 indicates an exact match, with the following equation:

Similarity=1−|2p−1|

From the similarity score, a simple acceptance threshold may be used toindicate the level of similarity which constitutes a biometric match.

In some embodiments, scanpath_fix characteristics are compared viapairwise distances between the centroids representing positions offixations at 248. In comparing two scanpaths, the Euclidean pairwisedistance may be calculated between the centroid positions of fixations.Following this, a tally may be made of the total number of fixationpoints in each set that could be matched to within 1° of at least onepoint in the opposing set. The similarity of scanpaths may be assessedby the proportion of tallied fixation points to the total number offixation points to produce a similarity score similar to those generatedfor the various eye movement metrics. In some embodiments, the totaldifference is normalized to produce a similarity score with a value of 0indicates no match and values of 1 indicates an exact match.

Iris similarity score 270 may be generated using iris templates 272. Inthis example, to produce similarity score 270, a Hamming distancecalculation is performed at 274.

Periocular similarity score 280 may be generated using perioculartemplates 282. Periocular similarity score 280 may be based perioculartemplate comparisons at 284.

At 250, weighted fusion module produces a combined similarity score viaa weighted sum of similarity scores produced by one or more of theindividual metrics. Weights for each individual metrics may be producedempirically. Other score level fusion techniques can be applied, e.g.,density-based score fusion techniques, transformation score fusion,classifier-based score fusion, methods that employ user-specific andevolving classification thresholds, and etc. The resulting similarityscore may be employed for the decision of match/non-match for scanpathauthentication or serves as an input to decision fusion module 222,which may combine, for example, OPC and CEM biometrics.

For example at 222, OPC similarity score 224 and CEM similarity score226 may be considered for final match/non-match decisions.Match/non-match decisions may be made based on one or more of thefollowing information fusion approaches:

-   -   Logical OR, AND. Logical fusion method employs individual        decisions from the OPC and scanpath modalities in a form of 1        (match) or 0 (non-match) to produce the final match/non-match        decision via logical OR (or AND) operations. In case of OR at        least one method should indicate a match for the final match        decision. In case of AND both methods should indicate a match        for the final match decision.    -   MIN, MAX. For a MIN (or MAX) method, the smallest (or largest)        similarity score may between the OPM and the scanpath        modalities. Thresholding may be applied to arrive to the final        decision. For example, if the resulting value is larger than a        threshold a match is indicated; otherwise, a non-match is        indicated.    -   Weighted addition. Weighted summation of the two or two        similarity scores from the OPC, CEM, iris, and periocular may be        performed via the formula p=w₁·A+w₂·B+w₃·C+w₄·D. Here p is the        resulting score, A, B, C and B stands for scores derived from        the OPC, CEM, Iris, and Periocular respectively. w1, w2, w3, w4        are corresponding weights. The resulting score p may be compared        with a threshold value. If p is greater than the threshold, a        match is indicated; otherwise, a non-match is indicated.    -   Other score level fusion techniques can be applied, e.g.,        density-based score fusion techniques, transformation score        fusion, classifier-based score fusion, methods that employ        user-specific and evolving classification thresholds, and etc.

FIG. 3 is a block diagram illustrating architecture for biometricauthentication via oculomotor plant characteristics according to oneembodiment. In certain embodiments, assessment using OPC as described inFIG. 3 may be combined with assessments based on CEM, irischaracteristics, periocular information, or some or all of those traits.In one embodiment, a biometric authentication is a based on acombination of OPC, CEM, iris characteristics, and periocularinformation.

Biometric authentication 300 may engage information during enrollment ofa user and, at a later time, authentication of the user. During theenrollment, the recorded eye movement signal from an individual issupplied to the Eye movement classification module 302. Eye movementclassification module 302 classifies the eye position signal 304 intofixations and saccades. A sequence of classified saccades' trajectoriesis sent to the oculomotor plant mathematical model (OPMM) 306.

Oculomotor plant mathematical model (OPMM) 306 may generate simulatedsaccades' trajectories based on the default OPC values that are groupedinto a vector with the purpose of matching the simulated trajectorieswith the recorded ones. Each individual saccade may be matchedindependently of any other saccade. Both classified and simulatedtrajectories for each saccade may be sent to error function module 308.Error function module 308 may compute error between the trajectories.The error result may trigger the OPC estimation module 310 to optimizethe values inside of the OPC vector minimizing the error between eachpair of recorded and simulated saccades.

When the minimum error is achieved for all classified and simulatedsaccade pairs, an OPC biometric template 312 representing a user may begenerated. The template may include a set of the optimized OPC vectors,with each vector representing a classified saccade. The number ofclassified saccades may determine the size of the user's OPC biometrictemplate.

During a person's verification, the information flow may be similar tothe enrollment procedure. Eye position data 314 may be provided to eyemovement classification module 302. In addition, the estimated userbiometrics template may be supplied to the person authentication module316 and information fusion module 318 to authenticate a user. Personauthentication module 316 may accept or reject a user based on therecommendation of a given classifier. Information fusion module 318 mayaggregate information related to OPC vectors. In some embodiments,information fusion module 318 may work in conjunction with the personauthentication module to authenticate a person based on multipleclassification methods. The output during user authentication proceduremay be a yes/no answer 320 about claimed user's identity.

Further description for various modules in this example is providedbelow.

Eye Movement Classification.

An automated eye movement classification algorithm may be used to helpestablish an invariant representation for the subsequent estimation ofthe OPC values. The goal of this algorithm is to automatically andreliably identify each saccade's beginning, end and all trajectorypoints from a very noisy and jittery eye movement signal (for example,as shown in FIG. 4. The additional goal of the eye movementclassification algorithm is to provide additional filtering for saccadesto ensure their high quality and a sufficient quantity of data for theestimation of the OPC values.

In one embodiment, a standardized Velocity-Threshold (I-VT) algorithm isselected due to its speed and robustness. A comparatively highclassification threshold of 70° per second may be employed to reduce theimpact of trajectory noises at the beginning and the end of eachsaccade. Additional filtering may include discarding saccades withamplitudes of less than 4°/s, duration of less than 20 ms, and varioustrajectory artifacts that do not belong to normal saccades.

Oculomotor Plant Mathematical Model.

The oculomotor plant mathematical model simulates accurate saccadetrajectories while containing major anatomical components related to theOP. In one embodiment, a linear homeomorphic 2D OP mathematical model isselected. The oculomotor plant mathematical model may be, for example,as described in O. V. Komogortsev and U. K. S. Jayarathna, “2DOculomotor Plant Mathematical Model for eye movement simulation,” inIEEE International Conference on BioInformatics and Bioengineering(BIBE), 2008, pp. 1-8. The oculomotor plant mathematical model in thisexample is capable of simulating saccades with properties resemblingnormal humans on a 2D plane (e.g. computer monitor) by consideringphysical properties of the eye globe and four extraocular muscles:medial, lateral, superior, and inferior recti. The following advantagesare associated with a selection of this oculomotor plant mathematicalmodel: 1) major anatomical components are accounted for and can beestimated, 2) linear representation simplifies the estimation process ofthe OPC while producing accurate simulation data within the spatialboundaries of a regular computer monitor, 3) the architecture of themodel allows dividing it into two smaller 1D models. One of the smallermodels becomes responsible for the simulation of the horizontalcomponent of movement and the other for the vertical. Such assignment,while producing identical simulation results when compared to the fullmodel, may allow a significant reduction in the complexity of therequired solution and allow simultaneous simulation of both movementcomponents on a multi-core system.

Specific OPC that may be accounted by the OPMM and selected to be a partof the user's biometric template are discussed below. FIG. 4 illustratesraw eye movement signal with classified fixation and saccades 400 and anassociated OPC biometric template 402. In the middle of FIG. 4,simulated via OPMM saccade trajectories generated with the OPC vectorsthat provide the closest matches to the recorded trajectories are shown.

In this example, a subset of nine OPC is selected as a vector torepresent an individual saccade for each component of movement(horizontal and vertical). Length tension (Klt=1.2 g/°)—the relationshipbetween the length of an extraocular muscle and the force it is capableof exerting, series elasticity (Kse=2.5)g/°)—resistive properties of aneye muscle while the muscle is innervated by the neuronal controlsignal, passive viscosity (Bp=0.06)g.s/°) of the eye globe, forcevelocity relationship—the relationship between the velocity of anextraocular muscle extension/contraction and the force it is capable ofexerting—in the agonist muscle (BAG=0.046 g-s/°), force velocityrelationship in the antagonist muscle (BANT=0.022 g-s/°), agonist andantagonist muscles' tension intercept (NFIX_C=14.0 g.) that ensures anequilibrium state during an eye fixation at primary eye position, theagonist muscle's tension slope (NAG_C=0.8 g.), and the antagonistmuscle's tension slope (NANT_C=0.5 g.), eye globe's inertia (J=0.000043g-s²/°). All tension characteristics may be directly impacted by theneuronal control signal sent by the brain, and therefore partiallycontain the neuronal control signal information.

The remaining OPC to produce the simulated saccades may be fixed to thefollowing default values: agonist muscle neuronal control signalactivation (11.7) and deactivation constants (2.0), antagonist muscleneuronal control signal activation (2.4) and deactivation constants(1.9), pulse height of the antagonist neuronal control signal (0.5 g.),pulse width of the antagonist neuronal control signal (PW_(AG)=7+|A|ms.), passive elasticity of the eye globe (Kp=N_(AG) _(—) _(C)−N_(ANT)_(—) _(C)) pulse height of the agonist neuronal control signal(iteratively varied to match recorded saccade's onset and offsetcoordinates), pulse width of the agonist neuronal control signal(PW_(ANT)=PW_(AG)+6).

The error function module provides high sensitivity to differencesbetween the recorded and simulated saccade trajectories. In some cases,the error function is implemented as the absolute difference between thesaccades that are recorded by an eye tracker and saccades that aresimulated by the OPMM.

R=Σ _(i=1) ^(n) |t _(i) −s _(i)|

where n is the number of points in a trajectory, t_(i) is a point in arecorded trajectory and s_(i) is a corresponding point in a simulatedtrajectory. The absolute difference approach may provide an advantageover other estimations such as root mean squared error (RMSE) due to itshigher absolute sensitivity to the differences between the saccadetrajectories.

First Example of an Experiment with Multimodal Ocular Authentication inwhich Only CEM & OPC Modalities are Employed

The following describes an experiment including biometric authenticationbased on oculomotor plant characteristics and complex eye movementpatterns.

Equipment.

The data was recorded using the EyeLink II eye tracker at samplingfrequency of 1000 Hz. Stimuli were presented on a 30 inch flat screenmonitor positioned at a distance of 685 millimeters from the subject,with screen dimensions of 640×400 millimeters, and resolution of2560×1600 pixels. Chin rest was employed to ensure high reliability ofthe collected data.

Eye Movement Recording Procedure.

Eye movement records were generated for participants' readings ofvarious excerpts from Lewis Carroll's “The Hunting of the Snark.” Thispoem was chosen for its difficult and nonsensical content, forcingreaders to progress slowly and carefully through the text.

For each recording, the participant was given 1 minute to read, and textexcerpts were chosen to require roughly 1 minute to complete.Participants were given a different excerpt for each of four recordingsession, and excerpts were selected from the “The Hunting of the Snark”to ensure the difficulty of the material was consistent, line lengthswere consistent, and that learning effects did not impact subsequentreadings.

Participants and Data Quality.

Eye movement data was collected for a total of 32 subjects (26 males/6females), ages 18-40 with an average age of 23 (SD=5.4). Mean positionalaccuracy of the recordings averaged between all calibration points was0.74° (SD=0.54°). 29 of the subjects performed 4 recordings each, and 3of the subjects performed 2 recordings each, generating a total of 122unique eye movement records.

The first two recordings for each subject were conducted during the samesession with a 20 minute break between recordings; the second tworecordings were performed a week later, again with a 20 minute breakbetween recordings.

Performance Evaluation.

The performance of the authentication methods was evaluated via FalseAcceptance Rate (FAR) and False Rejection Rate (FRR) metrics. The FARrepresents the percentage of imposters' records accepted as authenticusers and the FRR indicates the amount of authentic users' recordsrejected from the system. To simplify the presentation of the resultsthe Half Total Error Rate (HTER) was employed which was defined as theaveraged combination of FAR and FRR.

Performance of authentication using biometric assessment usingoculomotor plant characteristics, scanpaths, or combinations thereof,was computed as a result of a run across all possible combinations ofeye movement records. For example, considering 3 eye movement records(A, B, and C) produced by unique subjects, similarity scores wereproduced for the combinations: A+B, A+C, B+C. For the 122 eye movementrecords, this resulted in 7381 combinations that were employed foracceptance and rejection tests for both methods.

For this experiment, in case of the OPC biometrics, only horizontalcomponents of the recorded saccades with amplitudes >1° and durationover 4 ms were considered for the authentication. As a result averageamplitude of the horizontal component prior to filtering was 3.42°(SD=3.25) and after filtering was 3.79° (SD=3.26). Magnitude of thevertical components prior to filtering was quite small (M=1.2° SD=3.16),therefore vertical component of movement was not considered forderivation of OPC due to high signal/noise ratio of the verticalcomponent of movement.

Results.

Table I presents results of the experiment described above. In Table I,authentication results are presented for each biometric modality.Thresholds column contains the thresholds that produce minimum HTER forthe corresponding authentication approach. CUE refers tocounterfeit-resistant usable eye-based authentication, which may includeone of the traits, or two or more traits in combination that are basedon the eye movement signal.

TABLE I Method Name Thresholds FAR FRR HTER CUE = OPC p_(CUE) = 0.1 30%24% 27% CUE = CEM p_(CUE) = 0.5 26% 28% 27% CUE = (OPC) OR (CEM) p_(OPC)= 0.8 22% 24% 23% p_(s) = 0.6 CUE = (OPC) AND (CEM) p_(OPC) = 0.1 25%26% 25.5%   p_(s) = 0.2 CUE = MIN(OPC, CEM) p_(CUE) = 0.1 30% 24% 27%CUE = MAX(OPC, CEM) p_(CUE) = 0.6 25% 20% 22.5%   CUE = w₁□OPC + w₂□CEMp_(CUE) = 0.4 20% 18% 19% CUE = 0.5□(OPC) + 0.5□(CEM) p_(CUE) = 0.4 17%22% 19.5%  

FIG. 5 is a graph illustrating receiver operating curves (ROC) forocular biometric methods in the experiment described above. Each of ROCcurves 500 corresponds to a different modality and/or fusion approach.Curve 502 represents an authentication based on OPC. Curve 504represents an authentication based on CEM. Curve 506 represents anauthentication based on (OPC) OR (CEM). Curve 508 represents anauthentication based on (OPC) AND (CEM). Curve 510 represents anauthentication based on MIN (OPC, CEM). Curve 512 represents anauthentication based on MAX (OPC, CEM). Curve 514 represents anauthentication based on a weighted approach w1*OPC+w2*CEM.

Results indicate that OPC biometrics can be performed successfully for areading task, where the amplitude of saccadic eye movements can be largewhen compared to a jumping dot stimulus. In this example, both the OPCand CEM methods performed with similar accuracy providing the HTER of27%. Fusion methods were able to improve the accuracy achieving the bestresult of 19% in case of the best performing weighted addition (weightw₁ was 0.45 while weight w₂ was 0.55). Such results may indicateapproximately 30% reduction in the authentication error. In a customcase where weights for OPC and scanpath traits are equal, multimodalbiometric assessment was able to achieve HTER of 19.5%.

Second Example of an Experiment with Multimodal Ocular Authentication inwhich Only CEM & OPC & Iris Modalities are Employed.

The following describes an experiment including biometric authenticationbased on oculomotor plant characteristics, complex eye movementpatterns, and iris.

Equipment.

Eye movement recording and iris capture were simultaneously conductedusing PlayStation Eye web-camera. The camera worked at the resolution of640×480 pixels and the frame rate of 75 Hz. The existing IR pass filterwas removed from the camera and a piece of unexposed developed film wasinserted as a filter for the visible spectrum of light. An array of IRlights in a form of Clover Electronics IR010 Infrared Illuminatortogether with two separate IR diodes placed on the body of the camerawere employed for better eye tracking. The web-camera and main IR arraywere installed on a flexible arm of the Mainstays Halogen Desk Lamp eachto provide an installation that can be adjusted to a specific user. Achin rest that was already available from a commercial eye trackingsystem was employed for the purpose of stabilizing the head to improvethe quality of the acquired data. In a low cost scenario a comfortablechinrest can be constructed from very inexpensive materials as well.Stimulus was displayed on a 19 inch LCD monitor at a refresh rate of 60Hz. A web camera and other equipment such as described above may providea user authentication station at a relatively low cost.

Eye-Tracking Software.

ITU eye tracking software was employed for the eye tracking purposes.The software was modified to present required stimulus and store an eyeimage every three seconds in addition to the existing eye trackingcapabilities. Eye tracking was done in no-glint mode.

Stimulus.

Stimulus was displayed on a 19 inch LCD monitor with refresh rate of 60Hz. The distance between the screen and subjects' eyes was approximately540 mm. The complex pattern stimulus was constructed that employed theRorschach inkblots used in psychological examination, in order toprovide relatively clean patterns which were likely to evoke variedthoughts and emotions in participants. Inkblot images were selected fromthe original Rorschach psychodiagnostic plates and sized/cropped to fillthe screen. Participants were instructed to examine the imagescarefully, and recordings were performed over two sessions, with 3rotations of 5 inkblots per session. Resulting sequence of images was 12sec. long.

Eye movement data and iris data was collected for a total of 28 subjects(18 males, 10 females), ages 18-36 with an average age of 22.4 (SD=4.6).Each subject participated in two recording sessions with an interval ofapproximately 15 min. between the sessions.

Results.

Weighted fusion was employed to combine scores from all three biometricmodalities. The weights were selected by dividing the recorded datarandomly into training and testing sets. Each set contained 50% of theoriginal recording. After 20 random divisions the average results arepresented by Table II:

TABLE II Training Set - Testing Set - Average Performance AveragePerformance Method Name FAR FRR HTER FAR FRR HTER Ocular  22%  37% 25.526.2% 51.8%  39% Biometrics = OPC Ocular 27.2% 14.3%  20.7% 26.9% 28.927.9% Biometrics = CEM Ocular 16.9% 3.2% 10.1% 13.2% 13.9% 13.6%Biometrics = Iris Ocular  5.3% 1.4% 3.4% 7.6% 18.6% 13.1% Biometrics =w₁□OPC + w₂□CEM + w₃□Iris

FIG. 6 illustrates one embodiment of a system for allowing remotecomputing with ocular biometric authentication of a user. System 600includes user system 602, computing system 604, and network 606. Usersystem 602 is connected to user display device 608, user input devices610, and image sensor 611. Image sensor may be, for example, a web cam.User display device 608 may be, for example, a computer monitor.

Image sensor 611 may sense ocular data for the user, including eyemovement and external characteristics, such as iris data and periocularinformation and provide the information to user system 602.Authentication system 616 may serve content to the user by way of userdisplay device 608. Authentication system 616 may receive eye movementinformation, ocular measurements, or other information from user system602. Using the information received from user system 602, authenticationsystem 616 may assess the identity of the user. If the user isauthenticated, access to computing system 604 by the user may beenabled.

In the embodiment shown in FIG. 6, user system 602, computing system604, and authentication system 614 are shown as discrete elements forillustrative purposes. These elements may, nevertheless, in variousembodiments be performed on a single computing system with one CPU, ordistributed among any number of computing systems.

FIG. 7 illustrates one embodiment of a system for allowing remotecomputing with ocular biometric authentication of a user wearing aneye-tracking headgear system. System 620 may be similar to generallysimilar to system 600 described above relative to FIG. 6. To carry outauthentication, the user may wear eye tracking device 612. Eye trackingdevice 612 may include eye tracking sensors for one or both eyes of theuser. User system 610 may receive sensor data from eye tracking device612. Authentication system 616 may receive information from user system610 for authenticating the user.

Computer systems may, in various embodiments, include components such asa CPU with an associated memory medium such as Compact Disc Read-OnlyMemory (CD-ROM). The memory medium may store program instructions forcomputer programs. The program instructions may be executable by theCPU. Computer systems may further include a display device such asmonitor, an alphanumeric input device such as keyboard, and adirectional input device such as mouse. Computing systems may beoperable to execute the computer programs to implementcomputer-implemented systems and methods. A computer system may allowaccess to users by way of any browser or operating system.

Embodiments of a subset or all (and portions or all) of the above may beimplemented by program instructions stored in a memory medium or carriermedium and executed by a processor. A memory medium may include any ofvarious types of memory devices or storage devices. The term “memorymedium” is intended to include an installation medium, e.g., a CompactDisc Read Only Memory (CD-ROM), floppy disks, or tape device; a computersystem memory or random access memory such as Dynamic Random AccessMemory (DRAM), Double Data Rate Random Access Memory (DDR RAM), StaticRandom Access Memory (SRAM), Extended Data Out Random Access Memory (EDORAM), Rambus Random Access Memory (RAM), etc.; or a non-volatile memorysuch as a magnetic media, e.g., a hard drive, or optical storage. Thememory medium may comprise other types of memory as well, orcombinations thereof. In addition, the memory medium may be located in afirst computer in which the programs are executed, or may be located ina second different computer that connects to the first computer over anetwork, such as the Internet. In the latter instance, the secondcomputer may provide program instructions to the first computer forexecution. The term “memory medium” may include two or more memorymediums that may reside in different locations, e.g., in differentcomputers that are connected over a network. In some embodiments, acomputer system at a respective participant location may include amemory medium(s) on which one or more computer programs or softwarecomponents according to one embodiment may be stored. For example, thememory medium may store one or more programs that are executable toperform the methods described herein. The memory medium may also storeoperating system software, as well as other software for operation ofthe computer system.

The memory medium may store a software program or programs operable toimplement embodiments as described herein. The software program(s) maybe implemented in various ways, including, but not limited to,procedure-based techniques, component-based techniques, and/orobject-oriented techniques, among others. For example, the softwareprograms may be implemented using ActiveX controls, C++ objects,JavaBeans, Microsoft Foundation Classes (MFC), browser-basedapplications (e.g., Java applets), traditional programs, or othertechnologies or methodologies, as desired. A CPU executing code and datafrom the memory medium may include a means for creating and executingthe software program or programs according to the embodiments describedherein.

In some embodiments, collected CEM metrics are treated as statisticaldistributions, (rather than, for example, processing averages). In someembodiments, fusion techniques, such as random forest, are used.

As used herein, complex oculomotor behavior (“COB”) may be considered asa subtype of basic oculomotor behavior (fixations and saccades). Metricsfor COB (which is a part of the Complex Eye Movement Patterns) includesimple undershoot or overshoot, corrected undershoot/overshoot,multi-corrected undershoot/overshoot, compound saccades, and dynamicovershoot. In some cases, COB may include variant forms of basicoculomotor behavior, often indicating novel or abnormal mechanics.Examples of different forms of saccadic dysmetria, compound saccades,dynamic overshoot, and express saccades are described below. FIG. 8 is aset of graphs illustrating examples of complex oculomotor behavior.

Saccadic dysmetria is a common occurrence, in which a saccadeundershoots or overshoots the target stimulus. Often, if the dysmetriais too large, these saccades are followed by one or more smallcorrective saccades in the direction of the target. The type ofdysmetria may be identified based on these characteristics: undershoot,overshoot, simple (uncorrected), corrected (1 corrective saccade), andmulti-corrected (2 or more corrective saccades).

Compound saccades (also referred to as macrosaccadic oscillations) occuras a series of dysmetric saccades around a target. As such, compoundsaccades may be defined as a series of two or more corrective saccadesoccurring during a single stimulus, in which the direction of movementchanges (undershoot-overshoot-undershoot,overshoot-undershoot-overshoot, etc.)

Dynamic overshoot occurs as a small (0.25° to 0.5° amplitude),oppositely directed, post-saccadic corrective movement. Thesepost-saccadic movements may typically be merged with the precedingsaccade. As such, dynamic overshoot may be identified by projecting theabsolute distance travelled during the saccade onto the centroid of theprevious fixation; if the projected centroid exceeds the post-saccadefixation centroid by more than 0.5° (corresponding to a minimumovershoot of 0.25°), dynamic overshoot occurred may be considered tohave occurred.

Express saccades have an abnormally quick reaction time between theappearance of a stimulus and the onset of the saccade. Regular saccadesmay have a typical latency of 150 milliseconds; as such. As used herein,saccades with latency less than 150 milliseconds may be referred to as“express saccades”.

FIG. 8 present the examples of COB. x-axis=time in milliseconds;y-axis=position in degrees). d, p, q are detection thresholds. Specificnumbers relating to COB are provided herein for illustrative purposes.The COB metrics that numbers may vary from embodiment to embodiment, andspatial and temporal characteristics and the corresponding thresholdsmay also vary from embodiment to embodiment. In various embodiments, COB(for example, the frequency of the occurrence of various metrics thatcompose COB) is applied for the purposes of liveness testing, detectionof the physical and the emotional state of the user of the biometricsystem, or both.

Biometric Liveness Testing

As used herein, a “biometric liveness test” includes a test performed todetermine if the biometric sample presented to a biometric system camefrom a live human being. In some embodiments, a biometric liveness testis performed to determine if the biometric sample presented to thesystem is a live human being and is the same live human being who wasoriginally enrolled in the system (the “authentic live human being”).

In various embodiments, liveness detection built upon ocular biometricsframework is used to protect against spoof attacks. Some examples ofliveness detection in response to spoofing techniques are describedbelow. Although many of the embodiments are described for detecting to aparticular spoofing technique, any of the embodiments may be applied todetect any spoofing technique.

Spoofing Example 1 Spoofing is Done by High-Quality Iris Image Printedon Placard, Paper, Etc. and Presented to the Biometric System for theAuthentication or Identification

In this case, CEM (including COB) and OPC eye movement metrics areestimated. CEM related metrics may include fixation count, averagefixation duration, average vectorial average vertical saccade amplitude,average vectorial saccade velocity, average vectorial saccade peakvelocity, velocity waveform (Q), COB relatedmetrics—undershot/overshoot, corrected undershoot/overshoot,multi-corrected undershoot/overshoot, dynamic, compound, expresssaccades, scanpath length, scanpath area, regions of interest,inflection count, and slope coefficients of the amplitude-duration andmain sequence relationships; OPC-related length tension, serieselasticity, passive viscosity of the agonist and the antagonist muscle,force velocity relationship, the agonist and the antagonist muscles'tension intercept, the agonist muscle's tension slope, the antagonistmuscle's tension slope, eye globe's inertia, or combinations of one ormore of the above. Principal component analysis and/or linear/non-lineardiscriminant analysis may be performed. The values of the metrics may becompared to the normal human data via statistical tests (for example,t-test, Hoteling's T-square test, MANOVA). From this analysis, adetermination is made of whether a presented biometric sample is a fakeor it comes from the live-authentic user.

When the spoof is presented, extracted eye metrics may have abnormalvalues such as zero, or be negative, or, for example, would have alinear form, when non-linear form is the norm. Abnormality examples: a)only a single fixation is detected during template acquisition and/orfixation coordinates may indicate that it is directed outside of thescreen boundaries, b) no saccades are detected or saccades have theamplitudes close to zero, c) extracted OPC and CEM characteristics haveabnormally small or large values.

In some embodiments, once the biometric sample presented to a biometricsystem is determined to have come from a live human being, a livenesstest is used to determine whether the identified person is live humanbeing who was originally enrolled in the system. Person identificationof subject may be performed, for example, as described above relative toFIG. 2.

Spoofing Example 2 Spoofing is Done by Pre-Recording Eye MovementPattern on the Video Recording Device Such as Camera, Phone, Tablet,Etc.

In some embodiments, OPC and CEM modalities are used to extractcorresponding metrics. The combination of OPC and CEM may be used evenin cases when fully random stimulus is presented to the user forauthentication/identification, for example, a point of light that isjumping to the random locations on the screen. Each time the pattern ofwhat is presented to the user for authentication/identification may bedifferent, but the person may still able to be identified by the ocularbiometric system (for example, the system described in paragraph aboverelative to FIG. 2). Random characteristics of the stimuli may includespatial location of the presented target (for example, coordinates onthe screen) and temporal pattern (for example, the time when eachspecific jump of the target is presented). However, if the pre-recordedsequence is presented there will be a clear spatial and temporaldifference between the behavior of the stimulus and what waspre-recorded.

FIG. 9 illustrates a spoof attack via pre-recorded signal from theauthentic user. In the example shown in FIG. 9, the difference betweenthe estimated eye gaze locations from the pre-recorded signal of theauthentic user (spoof) and the locations of the stimulus that may bepresented during an authentication session. In this example, an intruderputs a pre-recorded video of the eye movements of the authentic user tothe sensor. The biometric system randomly changes presented pattern andthe estimations of the eye gaze locations from pre-recorded video missthe targets by large margins. In FIG. 9, spatial differences may bereadily observed. Solid line dots 700 represent locations of points thatwere actually presented to the user. Broken line dots 702 representestimated eye gaze locations that were estimated by processingpre-recorded eye movements of the authentic user to previous recordedsessions. Arrows between the pairs of dots represent positionaldifferences between what was presented and recorded. In this case, largedifferences clearly indicate that the presented sample is a spoof. Insome embodiments, spatial differences are checked as a Euclidiandistance metric between the presented locations and recorded from theuser.

In case of the spoof (pre-recorded eye movement sequence) the spatialand temporal difference may be large, which allows an easy distinctionbetween the spoof and the authentic signal. For example, FIG. 10illustrates the same figure for the authentic user. Solid line dots 704represent locations of points that were actually presented to the user.Broken line dots 706 represent estimated eye locations from an authenticuser. In the example illustrated in FIG. 10, an authentic user goesthrough the authentication process. Small positional differencesindicate that the recorded eye is able to follow presented randomstimulus and therefore it is not a pre-recorded presentation. Estimatedeye gazes from the user fall very closely to presented targets,identifying that a live eye is following the targets. Thus, comparingFIG. 9 and FIG. 10, the distances between the estimated eye gazes of thespoof and what is presented as a stimulus are large, while thedifferences between the estimated eye gazes from the live user and thestimulus locations are small.

In some embodiments, a similar approach to biometric authentication maybe applied in the time domain (for example, for biometric authenticationusing video). The timings of the appearances of flashing dots can berandomized and in this case pre-recorded eye movements may be out ofsync temporally with what is presented on the screen, introducing largedifferences between stimulus onsets the movements that are pre-recordedin the video sequence.

Spoofing Example 3 Spoofing is Done by an Accurate Mechanical Replica ofthe Human Eye

In some embodiments, differences in the variability between the replicaand the actual system are employed for spoof detection. To capture thevariability differences between live and spoof, covariance matrixes maybe built based on the OPC values estimated by an OPC biometricframework. Once such matrixes are constructed, a Principal ComponentAnalysis (PCA) may be performed to select a subset of characteristicsthat contain the bulk of the variability. The resulting OPC subset maybe employed to compute corresponding vector of eigen values. To make adecision if specific sample is live or a spoof, the maximum eigen valuein the vector may be compared to a threshold. When a value exceeds athreshold the corresponding biometric template is marked as a spoof Ifthe value is less than or equal to the threshold, the correspondingbiometric template may be marked as live.

In the case when an intruder steals the biometric database and performsspoofing with the mechanical replica of the eye created with theknowledge of the user's biometric template, the Correct Recognition rate(correct rate of the identification of the spoof or live sample) may beapproximately 85%.

In certain embodiments, a linear discriminant analysis (LDA) isperformed to determine the liveness based on the metrics using the OPCbiometric template. In certain embodiments, a multivariate analysis ofvariance (MANOVA) is performed to determine the liveness based on themetrics using the OPC biometric template.

Spoofing Example 4 Spoofing is Done by Imprinting High-Quality IrisImage on a Contact Lens and Putting on Top of the Intruders Live Eye

In a case when the iris part of the ocular biometrics system is spoofedby a contact lenses with imprinted pattern of the authentic user, theocular biometric system may use other modalities such as OPC, CEM, andperiocular features to make a distinction about the authenticity of theuser. Biometric performance of all biometric modalities other than theiris may be used to determine the authenticity of the user in the casewhen iris modality is completely spoofed.

In some embodiments (including, for example, the embodiments describedabove relative to Spoofing Examples 1-4), once the biometric samplepresented to a biometric system is determined to have come from a livehuman being, a liveness test may be used to determine whether theidentified person is live human being who was originally enrolled in thesystem. Person identification of subject may be performed, for example,as described above relative to FIG. 2.

In some embodiments, a user indicates a coercion attack to a system viaeye movement patterns. The eye movements may be pre-established beforethe coercion attack (for example, during training of the user). Signalsby a user using eye movement patterns may be done covertly or overtly.Signals by the user to ocular biometrics system via eye tracking may behard to detect by an intruder and will be non-intrusive. The eyetracking technology may be able to detect the direction of gaze with aprecision of approximately 0.5° of the visual angle. A human, while ableto tell the general location of the eye gaze and possibly count theamount of gaze shifts, cannot distinguish precisely where someone islooking.

Different types of authentication/identification stimuli such as imagescan be employed to allow the user to signal coercion attack in variousembodiments. For example, the following types of images may be employed:a) images containing a significant amount of rich textural informationacross the entire image, e.g., a forest or hills, b) images containingseveral separate zones of attention, e.g., structures, buildings, c)images with artificial content highlighting well defined focal points,e.g., blue and red balloons.

In various examples given below, each presented image type mayfacilitate a login process that would allow the user to fixate his/hereyes on the distinct focal points presented on the image to signal“normal” or “coercion” attack. For example, if the image of mountains ispresented during “normal” login, a user will look at the base of thehills, whereas during “coercion” entry the user will look at the pinetrees.

Difference in shapes (for example, scanpaths) as drawn by the eyes (i.e.spatial and temporal differences in the eye movement signatures) may beused to determine the difference between the “coercion” and “normallogin”. Examples are provided below.

FIG. 11 illustrates an example of the difference between “normal” and“coercion” logins. The light shaded scanpath indicates the scanpath fornormal login. The darker scanpath indicates the coercion login. Circlesrepresent fixations and lines represent saccades. The spatial locationsof the scanpaths may be different, however the number of fixations isthe same. The intruder would not be able to notice the differencebetween spatial locations, because the gaze would be directed on thesame screen, in the general vicinity of the presented picture. Alsocounting the change of the direction of the eye movement would not help,because both scanpaths have the same number of fixations and saccadesthat compose them.

Similarly to FIG. 11, FIG. 12 illustrates an example of the differencebetween “normal” and “coercion” logins. The light shaded scanpathindicates the scanpath for normal login. The darker scanpath indicatesthe coercion login. Circles represent fixations and lines representsaccades. The spatial locations of the scanpaths are different, howeverthe number of fixations is the same.

It is noted that even if an intruder hacks/steals the database of thebiometric templates of the system users and, for example, if theintruder knows that user has to make four fixations and four saccades tolog into the system, the information would not help the intruder todetect whether the user has executed the “coercion” sequence, becausethis sequence also contains four fixations and four saccades and byvisually observing the eye movements it would be impossible to determinewhich sequence a user actually executes. The intruder might count thenumber of rapid rotations of the eye (saccades), but not the spatiallocations of the resulting fixations.

Detection of the Physical and Emotional State of the Subject

An ocular biometrics system may provide information and services inaddition to determining the identity of a user. In some embodiments, thesystem is used to acquire information about the state of the subject. Invarious embodiments, indicators of the physical, emotional, healthstate, or whether a user is under the influence of alcohol/drugs, or acombination thereof, may be assessed.

In one embodiment, a system detects exhaustion of a user. Exhaustiondetection may be beneficial to systems that are installed inuser-operated machines such as cars, planes etc. In addition to theuser's identity, the system may detect fatigue and warn the user againstoperating machinery in such a state.

In an embodiment, an ocular biometric system detects, and assesses theseverity of a traumatic brain injury or a brain trauma such as aconcussion of the soldiers on the battlefield or from a sports injury(for example, when a soldier is injured as a result of the explosion orsome other occurrence).

Examples of states that may be detected using an ocular biometric systeminclude emotional states and physical states including, excessivefatigue, brain trauma, influence of substances or/and drugs, higharousal.

In some embodiments, metrics that are contained in OPC, CEM (includingCOB) categories are employed to detect the normality of a newly capturedtemplate. For example iris modality, periocular modality, OPC modalitymay indicate that user A is trying to authenticate into the system.However, metrics in the COB category may indicate excessive amount, ofundershoots, overshoots, or corrective saccade. This might be the caseof the excessive fatigue, because such “noisy” performance of the HumanVisual System is indicative of tiredness. Fatigue may be also indicatedby larger than normal amounts of express saccades and non-normalsaccades in terms of their main-sequence curve (i.e., saccade will havesmaller maximum velocity than during a normal saccade).

Cases of brain trauma may be detected as excessive variability presentin the metrics, for example, in values of the COB metrics. Statisticaltools as linear/non-linear discriminant analysis, principal componentanalysis, MANOVA, and other tests statistical tests may be employed todetect this excessive variability and make a decision about braintrauma. Maintaining a steady fixation against a stationary target andaccurately following smooth moving target may be employed for the braintrauma detection. In such cases distance and velocity metrics may beused to determine how well the target is fixated and how closely thesmoothly moving target is tracked.

Substance influence such as alcohol and drugs may be also determined bystatistically processing the metrics in the CEM and OPC templates. Forexample number of fixations and fixation durations (both metrics arepart of the CEM template) might be increased when a user is under theinfluence of drugs/alcohol when these metrics are compared to thealready recorded values.

In case of emotion detection such as arousal fixation duration might belonger than normal, large amounts of fixations might be exhibited.

The case of excessive fatigue, brain trauma, influence of substancesor/and drugs may be distinguished from the failure of liveness test. Incase of user exhaustion the ocular biometric system would extract OPC,CEM (including COB) metrics, or combinations thereof, and theircorresponding range would be close to normal values, even if the valuesare close to the top of the normal range. Extracted metrics that wouldfail the liveness test would likely have abnormal values, for example,negative, constant, close to zero, or values that are extremely large.

Biometric Identification via Miniature Eye Movements

In some embodiments, a system performs biometric identification usingminiature eye movements. Biometric identification via miniature eyemovements may be effected when a user is fixated just on a single dot.An eye movement that is called an eye fixation may be executed. Eyefixation may include three miniature eye movement types: tremor, drift,and micro-saccades (saccades with amplitudes of 0.5°). Assuming highpositional and temporal resolution of an eye tracker, OPC and CEMmetrics may be extracted from the micro saccades as from saccades withamplitudes larger than 0.5°. In addition, tremor characteristics such asfrequency and amplitude may be employed for the personidentification/authentication. Drift velocity and positionalcharacteristics may also be employed for the personidentification/authentication. In some embodiments, biometricidentification via miniature eye movements is performed by the same CEMmodules and is included in the regular CEM biometric template.

Biometric Identification via Saliency Maps

In some embodiments, a saliency map is generated based on recordedfixations. As used herein, a “saliency map” is a topographicallyarranged map that represents visual saliency of a corresponding visualscene.” Fixation locations may represent highlights of the saliency mapsor probabilistic distributions depending on the implementation. In thecase of a static image, all fixation locations may be employed to createnodes in the saliency map. In case of the dynamic stimuli, such asvideo, recorded fixations may be arranged in sliding temporal windows. Aseparate saliency may be created for each temporal window. Saliency maps(for example, driven by the fixations and/or other features of the eyemovement signal) may be stored as a part of an updated CEM template (forexample, based on the approach described in FIG. 13) may be compared bystatistical tests, such as Kullback-Leibler, to determine the similaritybetween the templates. The similarities/differences between thetemplates may be used to make decision about the identity of the user.

Biometric Assessment with Subject State Detection

FIG. 13 illustrates biometric assessment with subject state detectionand assessment. As used herein, “subject state characteristic” includesany characteristic that can be used to assess the state of a subject.States of a subject for which characteristics may be detected and/orassessed include a subject's physical state, emotional state, condition(for example, subject is alive, subject is under the influence of acontrolled substance), or external circumstances (for example, subjectis under physical threat or coercion). Many of the aspects of theassessment approach shown in FIG. 13 may be carried out in a similarmanner to that described above relative to FIG. 2. At 720, afterbiometric template generation but before biometric template matching viaindividual traits, state subject detection may be performed (forexample, to conduct detection related to liveness, coercion, physical,emotional, health states, and the detection of the influence of thealcohol and drugs.)

In some embodiments, a decision fusion module (for example, asrepresented by fusion module 222 shown in FIG. 13) may perform also aliveness check in a case when one of the modalities gets spoofed (forexample, the iris modality gets spoofed by the contact lens withimprinted iris pattern.)

In some embodiments, a system for person identification with biometricmodalities with eye movement signals includes liveness detection.Liveness detection may include estimation and analysis of OPC. In someembodiment liveness detection is used to prevent spoof attacks (forexample, spoof attacks that including generating an accurate mechanicalreplica of a human eye.) Spoof attack prevention may be employed for onefollowing classes of replicas: a) replicas that are built using defaultOPC values specified by the research literature, and b) replicas thatare built from the OPC specific to an individual.

In some embodiments, oculomotor plant characteristics (OPC) areextracted and a decision is made about the liveness of the signal basedon the variability of those characteristics.

In some embodiments, liveness detection is used in conjunction with irisauthentication devices is deployed in remote locations with possiblylittle supervision during actual authentication. Assuming that OPCcapture is enabled on the existing iris authentication devices by asoftware upgrade such devices will have enhanced biometrics and livenessdetection capabilities.

In some embodiments, a mathematical model of the oculomotor plantsimulates saccades and compares them to the recorded saccades extractedfrom the raw positional signal. Depending on the magnitude of theresulting error between simulated and recorded saccade, an OPCestimation procedure may be invoked. This procedure refines OPC with agoal of producing a saccade trajectory that is closer to the recordedone. The process of OPC estimation may be performed iteratively untilthe error is minimized. OPC values that produce this minimum form becomea part of the biometric template, which can be matched to an alreadyenrolled template by a statistical test (e.g. Hotelling's T-square).Once two templates are matched, the resulting score represents thesimilarity between the templates. The liveness detection module checksthe liveness of a biometric sample immediately after the OPC template isgenerated. A yes/no decision in terms of the liveness is made.

The modules used for the procedures in FIG. 13 may be implemented in asimilar manner to those described relative to FIG. 2. A livenessdetector and oculomotor plant mathematical models that can be employedfor creating a replica of a human eye in various embodiments aredescribed below.

Liveness Detector

The design of a liveness detector has two goals: 1) capture thedifferences between the live and the spoofed samples by looking at thevariability of the corresponding signals, 2) reduce the number ofparameters participating in the liveness decision.

Collected data indicates the feasibility of the goal one due to thesubstantial amount of the variability present in the eye movement signalcaptured from a live human and relatively low variability in the signalcreated by the replica. In addition to what was already statedpreviously about the complexity of the eye movement behavior and itsvariability. It is noted that the individual saccade trajectories andtheir characteristics may vary (to a certain extent) even in cases whenthe same individual makes them. This variability propagates to theestimated OPC, therefore, providing an opportunity to assess and scoreliveness.

To capture the variability differences between live and spoofed samplescovariance matrixes may be built based on the OPC values estimated bythe OPC biometric framework. Once such matrixes are constructed aPrincipal Component Analysis (PCA) is performed to select a subset ofcharacteristic that contains the bulk of the variability. A resultingOPC subset is employed to compute corresponding vector of eigen values.To make a decision if specific sample is live or a spoof the maximumeigen value in the vector is compared to a threshold. When a valueexceeds a threshold the corresponding biometric template is marked as aspoof and live otherwise.

Operation Modes of Eye Movement-Driven Biometric System 1. Normal Mode

In some embodiments, a video-based eye tracker is used as an eyetracking device. For each captured eye image, a pupil boundary and acorneal reflection from an IR light by the eye tracker are detected toestimate user's gaze direction.

During normal mode of operation of an eye movement-driven biometricsystem, a user goes to an eye tracker, represented by an image sensorand an IR light, and performs a calibration procedure. A calibrationprocedure may include a presentation of a jumping point of light on adisplay preceded by the instructions to follow the movements of the dot.During the calibration eye tracking software builds a set ofmathematical equations to translate locations of eye movement features(for example, pupil and the corneal reflection) to the gaze coordinateson the screen.

The process of the biometric authentication may occur at the same timewith calibration. Captured positional data during calibration proceduremay be employed to verify the identity of the user. However, a separateauthentication stimulus may be used following the calibration procedureif employment of such stimulus provides higher biometric accuracy.

2. Under Spoof Attack

To initiate a spoof attack, an attacker presents a mechanical replica tothe biometric system. The eye tracking software may detect two featuresfor tracking—pupil boundary and the corneal reflection. The replicafollows a jumping dot of light during the calibration/authenticationprocedure. The movements of the replica are designed to match naturalbehavior of the human visual system. A template may be extracted fromthe recorded movements. A liveness detector analyzes the template andmakes a decision if corresponding biometric sample is a spoof or not.

Mathematical Models of Human Eye

The eye movement behavior described herein is made possible by theanatomical structure termed the Oculomotor Plant (OP) and is representedby the eye globe, extraocular muscles, surrounding tissues, and neuronalcontrol signal coming from the brain. Mathematical models of differentcomplexities can represent the OP to simulate dynamics of the eyemovement behavior for spoofing purposes. The following describes threeOP models that may be employed in various embodiments.

Model I. Westheimer's second-order model represents the eye globe andcorresponding viscoelasticity via single linear elements for inertia,friction, and stiffness. Individual forces that are generated by thelateral and medial rectus are lumped together in a torque that isdependent on the angular eye position and is driven by a simplified stepneuronal control signal. The magnitude of the step signal is controlledby a coefficient that is directly related to the amplitude of thecorresponding saccade.

OPC employed for simulation. Westheimer's model puts inertia, friction,and stiffness in direct dependency to each other. In the experimentsdescribed herein, only two OPC—stiffness coefficient and stepcoefficient of the neuronal control signal—were varied to simulate asaccade's trajectory.

Model II. A fourth-order model proposed by Robinson employs neuronalcontrol signal in a more realistic pulse-step form, rather thansimplified step form. As a result the model is able to simulate saccadesof different amplitudes and durations, with realistic positionalprofiles. The model breaks OPC into two groups represented by the activeand passive components. The former group is represented by theforce-velocity relationship, series elasticity, and active state tensiongenerated by the neuronal control signal. The latter group isrepresented by the passive components of the orbit and the muscles in aform of fast and slow viscoelastic elements. All elements may beapproximated via linear mechanical representations (for example, linearsprings and voigt elements.)

OPC employed for simulation. In experiments described herein, thefollowing six parameters were employed for saccade's simulation in therepresentation: net muscle series elastic stiffness, net muscleforce-velocity slope, fast/slow passive viscoelastic elementsrepresented by spring stiffness and viscosity.

Model III is a fourth-order model by Komogortsev and Khan, which isderived from an earlier model of Bahill. This model represents eachextraocular muscle and their internal forces individually with aseparate pulse-step neuronal control signal provided to each muscle.Each extraocular muscle can play a role of the agonist—muscle pullingthe eye globe and the antagonist—muscle resisting the pull. The forcesinside of each individual muscle are: force-velocity relationship,series elasticity, and active state tension generated by the neuronalcontrol signal. The model lumps together passive viscoelasticcharacteristics of the eye globe and extraocular muscles into two linearelements. The model is capable of generating saccades with positional,velocity, and acceleration profiles that are close to the physiologicaldata and it is able to perform rightward and leftward saccades from anypoint in the horizontal plane.

OPC extracted for simulation: In experiments described herein, eighteenOPC were employed for the simulation of a saccade: length tensionrelationship, series elasticity, passive viscosity, force velocityrelationships for the agonist/antagonist muscles, agonist/antagonistmuscles' tension intercept, the agonist muscle's tension slope, and theantagonist muscle's tension slope, eye globe's inertia, pulse height ofthe neuronal control signal in the agonist muscle, pulse width of theneuronal control signal in the agonist muscle, four parametersresponsible for transformation of the pulse step neuronal control signalinto the active state tension, passive elasticity.

Experiment with Human Eye Replicas

Spoof attacks were conducted by the mechanical replicas simulated viathree different mathematical models representing human eye. The replicasvaried from relatively simple ones that oversimplify the anatomicalcomplexity of the oculomotor plant to more anatomically accurate ones.Two strategies were employed for the creation of the replicas. The firststrategy employed values for the characteristics of the oculomotor planttaken from the literature and the second strategy employed exact valuesof each authentic user. Results indicate that a more accurateindividualized replica is able to spoof eye movement-driven system moresuccessfully, however, even in this error rates were relatively low,i.e., FSAR=4%, FLRR=27.4%.

For spoofing purposes, a replica was made to exhibit most common eyemovement behavior that includes COB events. These events and theircorresponding parameters are illustrated by FIG. 8 and described below.

In this example, the onset of the initial saccade to the target occursin a 200-250 ms temporal window, representing typical saccadic latencyof a normal person. Each initial saccade is generated in a form ofundershoot or overshoot with the resulting error of random magnitude(p2) not to exceed 2° degrees of the visual angle. If the resultingsaccade's offset (end) position differs from the stimulus position bymore than 0.5° (p3) a subsequent corrective saccade is executed. Eachcorrective saccade is performed to move an eye fixation closer to thestimulus with the resulting error (p4) not to exceed 0.5°. The latency(p5) prior to a corrective saccade is randomly selected in a range100-130 ms. The durations of all saccades is computed via formula 2.2DOT A+21, where A represents saccade's amplitude in degrees of thevisual angle.

To ensure that spoofing attack produces accurate fixation behaviorfollowing steps are taken: 1) random jitter with amplitude (p6) not toexceed 0.05° is added to simulate tremor, 2) blink events are added withcharacteristics that resemble human behavior and signal artifactsproduced by the recording equipment prior and after blinks. The duration(p7) of each blink is randomly selected from the range 100-400 ms. Timeinterval between individual blinks is randomly selected in the 14-15sec. temporal window. To simulate signal artifacts introduced by the eyetracking equipment prior and after the blink, the positional coordinatesfor the eye gaze samples immediately preceding and following a blink areset to the maximum allowed recording range (±30° in our setup).

During a spoof attack, in this experiment, only horizontal components ofmovement are simulated. While generation of vertical and horizontalcomponents of movement performed by the HVS can be fully independent, itis also possible to witness different synchronization mechanisms imposedby the brain while generating oblique saccades. Even in cases when aperson is asked to make purely horizontal saccades it is possible todetect vertical positional shifts in a form of jitter and otherdeviations from purely horizontal trajectory. Consideration andsimulation of the events present in the vertical component of movementwould introduce complexity into the modeling process.

The goal of the stimulus was to invoke a large number of horizontalsaccades to allow reliable liveness detection. The stimulus wasdisplayed as a jumping dot, consisting of a grey disc sizedapproximately 1° with a small black point in the center. The dotperformed 100 jumps horizontally. Jumps had the amplitude of 30 degreesof the visual angle. Subjects were instructed to follow the jumping dot.

Two strategies that may be employed by an attacker to generate spoofsamples via described oculomotor plant models as described as follows:The first strategy assumes that the attacker does not have access to thestored OPC biometric template data. In this case the attacker employsthe default OPC values taken from the literature to build a singlemechanical replica of the eye to represent any authentic user. Thesecond strategy assumes that the attacker has stolen the database withstored OPC biometric templates and can employ OPC values to produce apersonalized replica for each individual to ensure maximum success ofthe spoof attack. In this case a separate replica is built for eachindividual by employing OPC averages obtained from the OPC biometrictemplates generated from all recordings of this person.

As a result the following spoofing attacks were considered. Spoof I-Aand Spoof II-A represent the attacks performed by the replica created bythe Model I and Model II respectively employing the first spoofgeneration strategy. Spoofs for the Models I and II created by thesecond strategy (i.e., Spoofs I-B, II-B), were not considered because ifthe corresponding OPC for the model I and II are derived from therecorded eye movement signal, then the saccades generated with resultingOPC are very different from normally exhibited saccades. Model IIIallows creating human-like saccades for both strategies, thereforeproducing attacks Spoof III-A and III-B.

The following metrics are employed for the assessment of livenessdetection and resistance to spoofing attacks.

$\begin{matrix}{{CR} = {100 \cdot \frac{{Correctly}\mspace{14mu} {Classified}\mspace{14mu} {Samples}}{{Total}\mspace{14mu} {Amount}\mspace{14mu} {Of}\mspace{14mu} {Samples}}}} & 1\end{matrix}$

Here CR is Classification Rate. CorrectlyClassifiedSamples is the numberof tests where OPC set was correctly identified as spoof or live.TotalAmountOfSamples is the total number of classified samples.

$\begin{matrix}{{FSAR} = {100 \cdot \frac{{Improper}\mspace{14mu} {Classified}\mspace{14mu} {Spoof}\mspace{14mu} {Samples}}{{Total}\mspace{14mu} {Amount}\mspace{14mu} {Of}\mspace{14mu} {Spoof}\mspace{14mu} {Samples}}}} & 2\end{matrix}$

Here FSAR is False Spoof Acceptance Rate. ImproperClassifiedSpoofSamplesis the number of spoof samples classified as live andTotalAmountOfSpoofSamples is the total amount of spoofed samples in thedataset.

$\begin{matrix}{{FLRR} = {100 \cdot \frac{{Improper}\mspace{14mu} {Classified}\mspace{14mu} {Live}\mspace{14mu} {Samples}}{{Total}\mspace{14mu} {Amount}\mspace{14mu} {Of}\mspace{14mu} {Live}\mspace{14mu} {Samples}}}} & 3\end{matrix}$

Here FLRR is False Live Rejection Rate. ImproperClassifiedLiveSamples isthe number of live samples that was marked by liveness detector as aspoof and TotalAmountOfLiveSamples is the total amount of live recordsin the dataset.

Table I shows results of the spoof detection experiment. Numbers in thetable represent percentages. “SD” represents standard deviation. Thesignal from live humans was captured at 1000 Hz with a high-gradecommercial eye tracking equipment, providing an opportunity to obtainthe OPC from a very high quality eye positional signal. The signal fromthe replica was generated also at a frequency of 1000 Hz.

TABLE I Spoof CR (SD) FSAR (SD) FLRR (SD) EER I-A   93 (3.9) 0 (0)  7.4(4.1) 5 II-A  80.3 (25.2) 0 (0) 11.8 (7)   8 III-A 86.4 (4.2) 0 (0) 15.5(4.6) 17 III-B 84.7 (4.1)   4 (5.2) 27.4 (4.1) 20

Biometric Assessment Using Statistical Distributions

In some embodiments, biometric techniques using on patterns identifiablein human eye movements are used to distinguish individuals. Thedistribution of primitive eye movement features is determined usingalgorithms based on one or more statistical tests. In variousembodiments, the statistical tests may include a Ansari-Bradley test, aMann-Whitney U-test, a two-sample Kolmogorov-Smirnov test, a two-samplet-test, or a two-sample Cramér-von Mises test. Score-level informationfusion may be applied and evaluated by one or more of the following:weighted mean, support vector machine, random forest, and likelihoodratio.

The distribution of primitive features inherent in basic eye movementscan be utilized to uniquely identify a given individual. Severalcomparison algorithms may be evaluated based on statistical tests forcomparing distributions, including: the two-sample t-test, theAnsari-Bradley test, the Mann-Whitney U-test, the two-sampleKolmogorov-Smirnov test, and the two-sample Cramér-von Mises test.Information fusion techniques may include score-level fusion by:weighted mean, support vector machine, random forest, and likelihoodratio.

CEM Biometric Framework

In one embodiment, a biometric assessment includes sensing, featureextraction, quality assessment matching, and decision making. In oneembodiment, different stages of the assessment are carried out indifferent modules. In one embodiment, a Sensor module processes the eyemovement signal, a Feature Extraction module identifies, filters, andmerges individual gaze points into fixations and saccades, a QualityAssessment module assesses the biometric viability of each recording, aMatching module generates training/testing sets and compares individualrecordings, and a Decision module calculates error rates under biometricverification and identification scenarios. These modules may be asfurther described below.

Sensor Module

The Sensor module may parse individual eye movement recordings,combining available left/right eye coordinates and removing invalid datapoints from the eye movement signal. Eye movement recordings are storedin memory as an eye movement database, with the eye movement signallinked to the experiment, trial, and subject that generated therecording.

Feature Extraction Module

The Feature Extraction module may generate feature templates for eachrecord in the eye movement database. Eye movement features are primarilycomposed of fixations and saccades. The eye movement signal is parsed toidentify fixations and saccades using an eye movement classificationalgorithm, followed by micro-saccade and micro-fixation filters.

Fixation and saccade groups are merged, identifying fixation-specificand saccade-specific features. Fixation features include: start time,duration, horizontal centroid, and vertical centroid. Saccade featuresinclude: start time, duration, horizontal amplitude, vertical amplitude,average horizontal velocity, average vertical velocity, horizontal peakvelocity, and vertical peak velocity.

Quality Assessment Module

The Quality Assessment may module identify the biometric viability ofthe generated feature templates. In this context, we utilize thefixation quantitative score, ideal fixation quantitative score, fixationqualitative score, and saccade quantitative score as tentative measureof the quality of features obtained from the recording.

Matching Module

The Matching module compares individual records, generating match scoresfor various metrics using comparison algorithms that operate on featuretemplates. In this case, comparison algorithms operate to compare thedistribution of fixation- and saccade-based features throughout eachrecord. Match scores from each comparison algorithm are then combinedinto a single match score with an information fusion algorithm.

The Matching module may partition records, splitting the database intotraining and testing sets by subject, according to a uniformly randomdistribution. Comparison and information fusion thresholds andparameters are generated on the training set, while error rates arecalculated on the testing set.

Decision Module

The Decision module may calculate error rates for comparison andinformation fusion under biometric verification and identificationscenarios. Under one verification scenario, each record in the testingset may be compared to every other record in the testing set exactlyonce, and false acceptance rate and true positive rate are calculated atvaried acceptance thresholds. Under one identification scenario, everyrecord in the testing set may be compared to every other record in thetesting set, and identification rates are calculated from the largestmatch score(s) from each of these comparison sets.

CEM Biometrics

In some embodiments, the following primitive eye movement may beassessed:

Start time (fixation)

Duration (fixation)

Horizontal centroid (fixation)

Vertical centroid (fixation)

Start time (saccade)

Duration (saccade)

Horizontal amplitude (saccade)

Vertical amplitude (saccade)

Horizontal mean velocity (saccade)

Vertical mean velocity (saccade)

Horizontal peak velocity (saccade)

Vertical peak velocity (saccade)

These features accumulate over the course of a recording, as thescanpath is generated. FIG. 14 illustrates a comparative distribution offixation over multiple recording sessions. By analyzing the distributionof these features throughout each recording, as shown in FIG. 14, thebehavior of the scanpath as a whole may be examined. At the same time,by considering the fixations and saccades that compose the scanpath,signal noise from the raw eye movement signal may be removed, and thedataset reduced to a computationally manageable size.

In some embodiments, to compare the distribution of primitive eyemovement features, multiple statistical tests are employed. Thesestatistical tests are applied as a comparison algorithm to thedistributions of each feature separately. The information fusionalgorithms may be applied to the match scores generated by eachcomparison algorithm to produce a single match score used for biometricauthentication.

The following are some comparison algorithms that may be applied invarious embodiments.

(C1) Two-Sample t-Test The two-sample t-test measures the probabilitythat observations from two recordings are taken from normaldistributions with equal mean and variance.

(C2) Ansari-Bradley Test

The Ansari-Bradley test measures the probability that observations fromtwo recordings with similar median and shape are taken fromdistributions with equivalent dispersion.

(C3) Mann-Whitney U-Test

The Mann-Whitney U-test measures the probability that observations fromtwo recordings are taken from continuous distributions with equalmedian.

(C4) Two-Sample Kolmogorov-Smirnov Test

The two-sample Kolmogorov-Smirnov test measures the probability thatobservations from two recordings are taken from the same continuousdistribution, measuring the distance between empirical distributions.

(C5) Two-Sample Cramér-von Mises Test

The two-sample Cramér-von Mises test measures the probability thatobservations from two recordings are taken from the same continuousdistribution, measuring the goodness-of-fit between empiricaldistributions.

The following are some information fusion algorithms that may be appliedin various embodiments.

(F1) Weighted Mean

The weighted mean algorithm combines the match scores produced forindividual metrics into a single match score on the interval [0, 1]. Thegenuine and imposter match score vectors of the training set are used toselect per-metric weighting which minimizes equal error rate viaiterative optimization, and the weighted mean produces a single matchscore as a linear combination of the match scores for each metric.

(F2) Support Vector Machine

The support vector machine algorithm classifies the match scoresproduced for individual metrics into a single match score in the set {0,1}. The support vector machine builds a 7th order polynomial on thegenuine and imposter match score vectors of the training set, and matchscores are classified by dividing them into categories separated by thepolynomial on an n-dimensional hyperplane.

(F3) Random Forest

The random forest algorithm combines the match scores produced forindividual metrics into a single match score on the interval [0, 1]. Anensemble of 50 regression trees is built on the genuine and impostermatch score vectors of the training set, and the random forestcalculates the combined match score based on a set of conditional rulesand probabilities.

(F4) Likelihood Ratio

The likelihood ratio algorithm combines the match scores produced forindividual metrics into a single match score on the interval [0, ∞). Thegenuine and imposter match score vectors of the training set are modeledusing Gaussian mixture models, and the likelihood ratio is calculated asthe ratio of the genuine probability density over the imposterprobability density.

Experiment to Evaluate Biometric Techniques

The following describes an experiment to evaluate biometric techniques.Biometric accuracy on both high- and low-resolution eye tracking systemswere used. Existing eye movement datasets collected by Komogortsev wereutilized for comparative evaluation, with collection methodology in thefollowing subsections.

Eye movement recordings were generated on both high-resolution andlow-resolution eye tracking systems using a textual stimulus pattern.The text of the stimulus was taken from Lewis Carroll's poem, “TheHunting of the Snark,” chosen for its difficult and nonsensical content,forcing readers to progress slowly and carefully through the text.

For each recording session, subjects were limited to 1 minute ofreading. To reduce learning effects, subjects were given a differentexcerpt from the text for each recording session and each excerpt wasselected to ensure that line lengths and the difficulty of material wereconsistent. As well, excerpts were selected to require approximately 1minute of active reading.

Eye movements were processed with the biometric framework describedabove, with eye movement classification thresholds: velocity thresholdof 20°/sec, micro-saccade threshold of 0.5°, and micro-fixationthreshold of 100 milliseconds. Feature extraction was performed acrossall eye movement recordings, while matching and information fusion wereperformed according to the methods described in herein. To assessbiometric accuracy, error rates were calculated under both verificationand identification scenarios.

Eye movement recordings were partitioned, by subject, into training andtesting sets according to a uniformly random distribution with a ratioof 1:1, such that no subject had recordings in both the training andtesting sets. Experimental results are averaged over 80 randompartitions for each metric, and 20 random partitions for each fusionalgorithm. Scores for the best performing algorithms are highlighted forreadability.

1. Verification Scenario

False acceptance rate is defined as the rate at which imposter scoresexceed the acceptance threshold, false rejection rate is defined as therate at which genuine scores fall below the acceptance threshold, andtrue positive rate is defined as the rate at which genuine scores exceedthe acceptance threshold. The equal error rate is the rate at whichfalse acceptance rate and false rejection rate are equal. FIGS. 15A and15B are graphs of the receiver operating characteristic in which truepositive rate is plotted against false acceptance rate for severalfusion methods. FIG. 15A is based on high resolution recordings. FIG.15B is based on low resolution recordings.

2. Identification Scenario

Identification rate is defined as the rate at which enrolled subjectsare successfully identified as the correct individual, where rank-kidentification rate is the rate at which the correct individual is foundwithin the top k matches. FIGS. 16A and 16B are graphs of the cumulativematch characteristic for several fusion methods, in which identificationrate by rank is plotted across all ranks The maximum rank is equivalentto the available comparisons. FIG. 16A is based on high resolutionrecordings. FIG. 16B is based on low resolution recordings.

Further modifications and alternative embodiments of various aspects ofthe invention may be apparent to those skilled in the art in view ofthis description. Accordingly, this description is to be construed asillustrative only and is for the purpose of teaching those skilled inthe art the general manner of carrying out the invention. It is to beunderstood that the forms of the invention shown and described hereinare to be taken as embodiments. Elements and materials may besubstituted for those illustrated and described herein, parts andprocesses may be reversed, and certain features of the invention may beutilized independently, all as would be apparent to one skilled in theart after having the benefit of this description of the invention.Methods may be implemented manually, in software, in hardware, or acombination thereof. The order of any method may be changed, and variouselements may be added, reordered, combined, omitted, modified, etc.Changes may be made in the elements described herein without departingfrom the spirit and scope of the invention as described in the followingclaims.

1. A method of assessing a person's identity, comprising: measuring eyemovement of the person; estimating one or more anatomicalcharacteristics of an oculomotor plant of the person based at least inpart on at least a portion of the measured eye movement; estimating oneor more brain's control strategies in guiding visual attention viaexhibition of complex eye movement patterns represented in part byspatial paths of the eye computed from the measured eye movement; andassessing the person's identity based in part on at least one of theestimated one or more characteristics of the oculomotor plant of theperson, and based in part on one or more properties related to thecomplex eye movement patterns.
 2. The method of claim 1, whereinmeasuring eye movement of the person comprises eye tracking.
 3. Themethod of claim 1, wherein estimating at least one of the anatomicalcharacteristics of an oculomotor plant of the person comprises creatinga two-dimensional mathematical model including at least one of theanatomical characteristics of the oculomotor plant.
 4. The method ofclaim 1, further comprising generating one or more brain's controlstrategies in guiding visual attention from eye movements of the personin a form of complex eye movement patterns, wherein the assessment ofthe person's identity is based in part on one or more components ofcomplex eye movement patterns.
 5. The method of claim 1, whereinassessing the person's identity comprises matching the biometrictemplates related to complex eye movement patterns and oculomotor plantcharacteristics with previously acquired templates for the person. 6.The method of claim 1, wherein assessing the person's identity comprisesdetermining the identity of the person.
 7. The method of claim 1,wherein further comprising authenticating the person based on theassessment of the person's identity.
 8. The method of claim 1, whereinestimating at least one of the one or more characteristics of anoculomotor plant of the person comprises estimating at least one staticcharacteristic of the oculomotor plant.
 9. The method of claim 1,wherein estimating at least one of the one or more characteristics of anoculomotor plant of the person comprises estimating at least one dynamiccharacteristic of the oculomotor plant.
 10. The method of claim 1,further comprising measuring one or more external characteristics of atleast one eye of the person, wherein the assessment of the person'sidentity is based in part on at least one of the measuredcharacteristics.
 11. The method of claim 10, wherein measuring one ormore external characteristics comprises measuring one or morecharacteristics of an iris or a periocular region of the eye of theperson.
 12. The method of claim 10, wherein measuring one or moreexternal characteristics comprises measuring one or more characteristicsof an iris and one or more characteristics of a periocular region of theeye of the person.
 13. The method of claim 1, wherein at least somemeasurements of eye movement of the person are performed while theperson is reading, looking at images, webpages, or interacting with acomputer, wherein estimating at least one of the one or morecharacteristics of the oculomotor plant of the person is based onmeasured eye movement while the person is reading, looking at images,webpages, or interacting with a computer.
 14. The method of claim 1,further comprising accessing one or more oculomotor plant characteristictemplates for the person, wherein the assessment of the person'sidentity is based in part on a comparison of eye movement with at leastone of the one or more oculomotor plant characteristic templates. 15.The method of claim 1, wherein at least one of the oculomotor plantcharacteristic templates is based at in part on previous measurements ofeye movement of the person.
 16. The method of claim 1, wherein at leastone estimate of the one or more characteristics of the oculomotor plantof the person is based at least in part on an oculomotor plantmathematical model.
 17. The method of claim 1, further comprisingproducing a minimum error between one or more recorded signals and oneor more simulated signals.
 18. The method of claim 1, wherein assessingthe person's identity comprises applying one or more statistical teststo one or more oculomotor plant characteristics. 19-25. (canceled)
 26. Asystem, comprising: an instrument configured to measure eye movement ofa person and one or more characteristics of an eye of the person; and aprocessor; a memory coupled to the processor, wherein the memorycomprises program instructions executable by the processor to implement:measuring eye movement of the person with the instrument; estimating oneor more characteristics of an oculomotor plant of the person based atleast in part on at least a portion of the measured eye movement;estimating one or more properties of the complex eye movement patternsbased at least in part on at least a portion of the measured eyemovement; and assessing the person's identity based in part on at leastone of the estimated one or more characteristics of the oculomotor plantof the person, and based in part on at least one of the estimated one ormore complex eye movement patterns. 27-30. (canceled)
 31. Anon-transitory, computer-readable storage medium comprising programinstructions stored thereon, wherein the program instructions areconfigured to implement: measuring eye movement of the person;estimating one or more characteristics of an oculomotor plant of theperson based at least in part on at least a portion of the measured eyemovement; estimating one or more complex eye movement patterns of theeye based at least in part on at least a portion of the measured eyemovement; and assessing the person's identity based in part on at leastone of the estimated one or more characteristics of the oculomotor plantof the person, and based in part on at least one of the estimated one ormore complex eye movement patterns of the eye. 32-65. (canceled)